Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic
Authors: Gary E. Weissman, Andrew Crane-Droesch, Corey Chivers, Mark E. Mikkelsen, Scott D. Halpern
Journal: Annals of Internal Medicine (2020)

Letters20 October 2020Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 PandemicFREEGary E. Weissman, MD, MSHP, Andrew Crane-Droesch, PhD, Corey Chivers, PhD, Mark E. Mikkelsen, MD, MSCE, and Scott D. Halpern, MD, PhDGary E. Weissman, MD, MSHPUniversity of Pennsylvania, Philadelphia, Pennsylvania (G.E.W., M.E.M., S.D.H.)Search for more papers by this author, Andrew Crane-Droesch, PhDUniversity of Pennsylvania and Penn Medicine Predictive Healthcare, Philadelphia, Pennsylvania (A.C.)Search for more papers by this author, Corey Chivers, PhDPenn Medicine Predictive Healthcare, Philadelphia, Pennsylvania (C.C.)Search for more papers by this author, Mark E. Mikkelsen, MD, MSCEUniversity of Pennsylvania, Philadelphia, Pennsylvania (G.E.W., M.E.M., S.D.H.)Search for more papers by this author, and Scott D. Halpern, MD, PhDUniversity of Pennsylvania, Philadelphia, Pennsylvania (G.E.W., M.E.M., S.D.H.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-1062 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:Dr. Stern highlights 2 important limitations of the SIR model that underlie the CHIME planning tool. We agree that the cohort structure—defined by differences in demographic characteristics, disease response, and contact patterns, among others—influences the course and severity of the pandemic. Using an agent-based model with a first-in, first-out process as Dr. Stern suggests would permit the representation of some of these cohort characteristics. However, this approach would come at the cost of increased complexity, with accompanying difficulty in identifying reliable parameter estimates based on limited published data early in the pandemic.The SIR assumption of a constant doubling time does not reflect the effects of rapidly changing physical distancing behaviors and other policies that might alter viral transmission. Thus, we have advised that the CHIME tool be used only for reliable short-term forecasts. We advocate that users of CHIME and any other COVID-19–related pandemic model iteratively review all parameters based on empirical observations of the pandemic course.As our model has been iteratively revised since publication, the subsequent CHIME estimates have also indicated an earlier but not higher peak, as suggested by Dr. Stern's calculations. However, we disagree that the direction of SIR model bias is yet definitely known in the case of COVID-19. The cited article suggests that the SIR model underestimates model peak and timing compared with 1 matrix model (1). At the same time, other work has suggested that SIR models may overestimate total epidemic size compared with contact models that account for social network structure (2). Further work is needed to validate all COVID-19–related pandemic models against both each other and empirically observed counts of infected cases and their subsequent care utilization patterns. As such, our original manuscript included a plan to undertake such an empirical validation.At this time, we again acknowledge the limitations of SIR models and have since extended CHIME to account for more complex dynamics as more data have become available (https://penn-chime.phl.io). The performance of CHIME has improved with updating since publication, and CHIME has so far proved useful to guide planning efforts in our health system. However, we will continue to revise and assess the model in order to apply these lessons to more efficiently and effectively model future epidemics.References1. Grant A. Dynamics of COVID-19 epidemics: SEIR models underestimate peak infection rates and overestimate epidemic duration. medRxiv. Preprint posted online 12 April 2020. doi:10.1101/2020.04.02.20050674 Google Scholar2. Hébert-Dufresne L, Althouse BM, Scarpino SV, et al. Beyond R0 : the importance of contact tracing when predicting epidemics. arXiv. Preprint posted online 10 February 2020. Accessed at http://arxiv.org/abs/2002.04004 on 10 April 2020. Google Scholar Comments 0 Comments Sign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Gary E. Weissman, MD, MSHP; Andrew Crane-Droesch, PhD; Corey Chivers, PhD; Mark E. Mikkelsen, MD, MSCE; Scott D. Halpern, MD, PhDAffiliations: University of Pennsylvania, Philadelphia, Pennsylvania (G.E.W., M.E.M., S.D.H.)University of Pennsylvania and Penn Medicine Predictive Healthcare, Philadelphia, Pennsylvania (A.C.)Penn Medicine Predictive Healthcare, Philadelphia, Pennsylvania (C.C.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-1260. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoLocally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic Gary E. Weissman , Andrew Crane-Droesch , Corey Chivers , ThaiBinh Luong , Asaf Hanish , Michael Z. Levy , Jason Lubken , Michael Becker , Michael E. Draugelis , George L. Anesi , Patrick J. Brennan , Jason D. Christie , C. William Hanson III , Mark E. Mikkelsen , and Scott D. Halpern Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic Ralph H. Stern Metrics Cited byAn operations research approach to automated patient scheduling for eye care using a multi-criteria decision support toolRepatriation of Transferred Patients: A Solution for Hospital Capacity Concerns?Analysis and simulation of a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures: A case study of the outbreak in Urumqi, China in August 2022Digital public health leadership in the global fight for health securitySimultaneous response to multiple disasters: Integrated planning for pandemics and large-scale earthquakesOrganisation et monitoring des capacités hospitalières en période de criseAnalyses on ICU and non-ICU capacity of government hospitals during the COVID-19 outbreak via multi-objective linear programming: An evidence from IstanbulOn the prediction of isolation, release, and decease states for COVID-19 patients: A case study in South KoreaHybrid modelling and simulation of the effect of vaccination on the COVID-19 transmissionUnderstanding Health Care Administrators’ Data and Information Needs for Decision Making during the COVID-19 Pandemic: A Qualitative Study at an Academic Health SystemPotential Risk Factors for Length of Hospitalization in COVID-19 Patients: A Cross-sectional StudyThe health-related quality of life in Iranian patients with COVID-19Ethical considerations and patient safety concerns for cancelling non-urgent surgeries during the COVID-19 pandemic: a reviewInteractive Visualization for Statistical Modelling through a Shiny App in RSimulating and Predicting the Active Cases and Hospitalization Considering the Second Wave of COVID-19Memory is key in capturing COVID-19 epidemiological dynamicsAn observational study to develop a scoring system and model to detect risk of hospital admission due to COVID‐19Forecasting COVID-19 daily cases using phone call dataDemographic and public health characteristics explain large part of variability in COVID-19 mortality across countriesMachine learning for optimizing healthcare resourcesSocial distancing and testing as optimal strategies against the spread of COVID-19 in the Rio Grande Valley of TexasAutomatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural NetworksUsing Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and MexicoTelemedicine in haemophilia during COVID-19 and beyond: a comprehensive reviewExpanding Hospital Capacity during the COVID-19 Pandemic: The Family Voice MattersPandemic spread prediction and healthcare preparedness through financial and mobility dataA Warning System for the Return of COVID-19 Using Social Media DataCOVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventionsTocilizumab Treatment for Cytokine Release Syndrome in Hospitalized Patients With Coronavirus Disease 2019COVID-19 Models for Hospital Surge Capacity Planning: A Systematic ReviewInterprofessional/interdisciplinary teamwork during the early COVID-19 pandemic: experience from a children’s hospital within an academic health centerExtracorporeal Membrane Oxygenation During the Coronavirus Disease 2019 Pandemic*The Urge to Build More Intensive Care Unit Beds and Ventilators: Intuitive but ErrantScott D. Halpern, MD, PhD and Franklin G. Miller, PhDCOVID-19 Nonessential Surgery Restrictions and Spine SurgeryAn interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real timeCOVID-19 e saúde mental: a emergência do cuidado 20 October 2020Volume 173, Issue 8 Page: 680-681 Keywords Behavior COVID-19 Disclosure Forecasting ePublished: 20 October 2020 Issue Published: 20 October 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...